Doctoral candidate, Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, MA, USA,
Research associate, Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Int Perspect Sex Reprod Health. 2020 Apr 15;46:21-33. doi: 10.1363/46e8520.
Many community-based reproductive health programs use their program data to monitor progress toward goals. However, using such data to assess programmatic impact on outcomes such as contraceptive use poses methodological challenges. Inverse probability weighting (IPW) may help overcome these issues.
Data on 33,162 women collected in 2013-2015 as part of a large-scale community-based reproductive health initiative were used to produce population-level estimates of the contraceptive prevalence rate (CPR) and modern contraceptive prevalence rate (mCPR) among married women aged 15-49 in Pakistan's Korangi District. To account for the nonrandom inclusion of women in the sample, estimates of contraceptive prevalence during the study's four seven-month intervention periods were made using IPW; these estimates were compared with estimates made using complete case analysis (CCA) and the last observation carried forward (LOCF) method-two approaches for which modeling assumptions are less flexible.
In accordance with intervention protocols, the likelihood that women were visited by intervention personnel and thus included in the sample differed according to their past and current contraceptive use. Estimates made using IPW suggest that the CPR increased from 51% to 64%, and the mCPR increased from 34% to 53%, during the study. For both outcomes, IPW estimates were higher than CCA estimates, were generally similar to LOCF estimates and yielded the widest confidence intervals.
IPW offers a powerful methodology for overcoming estimation challenges when using program data that are not representative of the population in settings where cost impedes collection of outcome data for an appropriate control group.
许多基于社区的生殖健康项目利用其项目数据来监测实现目标的进展。然而，使用这些数据来评估项目对避孕使用等结果的影响存在方法学挑战。逆概率加权（Inverse Probability Weighting，简称 IPW）可能有助于克服这些问题。
使用 2013-2015 年作为一项大型基于社区的生殖健康倡议的一部分收集的 33162 名妇女的数据，生成巴基斯坦科尔干地区 15-49 岁已婚妇女的避孕普及率（Contraceptive Prevalence Rate，简称 CPR）和现代避孕普及率（Modern Contraceptive Prevalence Rate，简称 mCPR）的人群水平估计值。为了考虑到妇女在样本中的非随机纳入，使用 IPW 对研究的四个七个月干预期间的避孕普及率进行了估计；这些估计值与使用完整病例分析（Complete Case Analysis，简称 CCA）和末次观测值结转（Last Observation Carried Forward，简称 LOCF）方法进行的估计值进行了比较，这两种方法的建模假设不太灵活。
根据干预方案，干预人员访问妇女的可能性以及因此被纳入样本的可能性因她们过去和当前的避孕使用情况而异。使用 IPW 进行的估计表明，在研究期间，CPR 从 51%增加到 64%，mCPR 从 34%增加到 53%。对于这两个结果，IPW 估计值均高于 CCA 估计值，通常与 LOCF 估计值相似，且产生的置信区间最宽。
在不具有代表性的情况下，当使用项目数据且成本妨碍为适当的对照组收集结果数据时，IPW 为克服估计挑战提供了一种强大的方法。
